They can keep their favorite spreadsheets, avoid the arduous transition to a financial software package, and work within an enterprise environment that upholds corporate data quality initiatives.
What does CDQ stand for?
CDQ stands for Corporate Data Quality
This definition appears somewhat frequently and is found in the following Acronym Finder categories:
- Business, finance, etc.
See other definitions of CDQ
We have 16 other meanings of CDQ in our Acronym Attic
- Career Development Quarterly (National Career Development Association)
- Cerium Doped Quartz
- Chebyshev Differential Quadrature
- Client Diagnostic Questionnaire (Mental Health Screening Tool)
- Coke Dry Quenching (method)
- Communication Design Quarterly (publication)
- Community Development Quota
- Compact Disc Quality
- Conseil de Quartier (French: Council District)
- Convert Doubleword to Quadword (Win32 assembly)
- Costume Designers' Guild (cinema)
- Coût Délai Qualité (French: Cost Time Quality)
- Customer Data Quality
- California Dairy Quality Assurance (agriculture program)
- Customer Driven Quality Award (Ford Motor Company)
- California Dairy Quality Assurance Program
- Combat Divers Qualification Course
- Community Development Queensland Institute (Brisbane, Australia)
- Customer Delight, Quality Logistics
- Critical Design and Qualification Review (NASA)
Samples in periodicals archive:
Key drivers of data complexity A combination of trends is accelerating the need to manage data migration activity more effectively as part of a corporate data quality strategy: * Mergers, acquisitions and restructuring of disparate sytems * Compliance--the need to validate data against regulations and standards such as Basel II and Sarbanes-Oxley (SOX) * Data volume--escalating data is increasing the burden of managing data * Data diversity--introduction of data in new formats e.
During the Webinar, Hayler will analyze key outcomes of The Information Difference's data quality survey and share professional recommendations based on real-world case studies to help businesses mitigate the following: * The business case for data quality to gain executive support; * Lessons from real-life corporate data quality disasters; * The critical elements of a successful data quality strategy; * The role of data governance; and * How to manage inconsistent corporate data.